Data analytics is increasingly seen as a promising tool for effective integrity and corruption risk management, as it can support a robust due diligence process and early identification of potential integrity-related risks. Consequently, companies are interested in making better use of their internal data to monitor integrity risks and identify new threats.

With this in mind, the Internal Integrity Risk Warning System (IIRWiS) project aims to advance integrity management by applying methods developed for Deep Learning and natural language processing to the integrity domain. The project will develop machine learning models based on text-based data sources (internal documents and digital communications) that are capable of automatically recognising integrity-related behaviours. In addition, the project will evaluate the challenges of analysing internal company data and explore options for action for an ethical approach.

Publication

Organizational Corruption, Crime and Covid-19: Upholding Integrity and Transparency in Times of Crises

Corruption often flourishes in times of uncertainty and crisis. It is not surprising, therefore, that there have been a number of cases of corruption during the coronavirus pandemic. For example, Swiss businesspeople were involved in deals involving protective materials that are still under investigation by public prosecutors. The academic analysis of the pandemic and its side effects opens up the possibility of developing better protection against corruption.

The book Organizational Corruption, Crime and Covid-19 brings together leading international experts to draw lessons from the coronavirus crisis to improve transparency, integrity, trust and governance in the future. Based on research and case studies, practical examples of methods, approaches and tools to combat corruption in the public and private sectors are presented.

Special attention is given to the use of artificial intelligence (AI) in the fight against corruption. In his book chapter, Christian Hauser analyzes the potential of digital tools and AI in the fight against corruption. He presents a digital compliance management cycle consisting of four main components: prevention, detection, response and prediction. It becomes clear that digital and AI-based tools can make a significant contribution to the fight against corruption, especially by automating processes and analyzing large amounts of data. This makes it possible to identify patterns and anomalies related to corrupt practices. At the same time, it is important to understand the limitations of using AI in the fight against corruption. These limitations arise in particular from the ethical and social issues associated with the use of AI. One example is the protection of privacy.

Parties involved

The project has been implemented by the Swiss Institute for Entrepeneurship (SIFE), in cooperation with the Swiss Institute for Information Science (SII) and the PRME Business Integrity Action Center